Overview

Dataset statistics

Number of variables55
Number of observations3296031
Missing cells8034621
Missing cells (%)4.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 GiB
Average record size in memory440.0 B

Variable types

CAT47
NUM5
BOOL3

Warnings

Data Type has constant value "3296031" Constant
Visibility has constant value "3296031" Constant
COCOM_EVENTDATA_DATA_IPADDRESS has constant value "3296031" Constant
EVENTDATA_DATA_LOGONTYPE has constant value "3296031" Constant
EVENTDATA_DATA_PROCESSID has constant value "3296031" Constant
EVENTDATA_DATA_SUBJECTLOGONID has constant value "3296031" Constant
EVENTDATA_DATA_SUBJECTUSERSID has constant value "3296031" Constant
SYSTEM_CHANNEL has constant value "3296031" Constant
SYSTEM_EVENTID has constant value "3296031" Constant
SYSTEM_KEYWORDS has constant value "3296031" Constant
SYSTEM_LEVEL has constant value "3296031" Constant
SYSTEM_OPCODE has constant value "3296031" Constant
SYSTEM_PROVIDER_GUID has constant value "3296031" Constant
SYSTEM_PROVIDER_NAME has constant value "3296031" Constant
SYSTEM_TASK has constant value "3296031" Constant
SYSTEM_VERSION has constant value "3296031" Constant
EVENTDATA_DATA_IPADDRESS has a high cardinality: 473 distinct values High cardinality
EVENTDATA_DATA_LOGONGUID has a high cardinality: 126003 distinct values High cardinality
EVENTDATA_DATA_TARGETLOGONID has a high cardinality: 3295757 distinct values High cardinality
EVENTDATA_DATA_TARGETUSERNAME has a high cardinality: 984 distinct values High cardinality
EVENTDATA_DATA_TARGETUSERNAME_LCASE has a high cardinality: 984 distinct values High cardinality
EVENTDATA_DATA_TARGETUSERSID has a high cardinality: 1059 distinct values High cardinality
EVENTDATA_DATA_WORKSTATIONNAME has a high cardinality: 264 distinct values High cardinality
FILENAME_INGEST has a high cardinality: 3881 distinct values High cardinality
LOCAL_SYSTEM_TIMECREATED_SYSTEMTIME has a high cardinality: 3170345 distinct values High cardinality
LOCAL_TIMESTAMP has a high cardinality: 3170345 distinct values High cardinality
SITE_COLLECTION has a high cardinality: 97 distinct values High cardinality
SYSTEM_COMPUTER has a high cardinality: 97 distinct values High cardinality
SYSTEM_COMPUTER_REVERSE has a high cardinality: 97 distinct values High cardinality
SYSTEM_TIMECREATED_SYSTEMTIME has a high cardinality: 3170345 distinct values High cardinality
LONGITUDE_EVENTDATA_DATA_IPADDRESS is highly correlated with LATITUDE_EVENTDATA_DATA_IPADDRESSHigh correlation
LATITUDE_EVENTDATA_DATA_IPADDRESS is highly correlated with LONGITUDE_EVENTDATA_DATA_IPADDRESSHigh correlation
EVENTDATA_DATA_KEYLENGTH is highly correlated with EVENTDATA_DATA_AUTHENTICATIONPACKAGENAME and 1 other fieldsHigh correlation
EVENTDATA_DATA_AUTHENTICATIONPACKAGENAME is highly correlated with EVENTDATA_DATA_KEYLENGTH and 1 other fieldsHigh correlation
EVENTDATA_DATA_LOGONPROCESSNAME is highly correlated with EVENTDATA_DATA_AUTHENTICATIONPACKAGENAME and 1 other fieldsHigh correlation
EVENTDATA_DATA_TARGETDOMAINNAME is highly correlated with COUNTRY_EVENTDATA_DATA_IPADDRESS and 6 other fieldsHigh correlation
COUNTRY_EVENTDATA_DATA_IPADDRESS is highly correlated with EVENTDATA_DATA_TARGETDOMAINNAME and 9 other fieldsHigh correlation
EVENTDATA_DATA_LMPACKAGENAME is highly correlated with EVENTDATA_DATA_TARGETDOMAINNAME and 2 other fieldsHigh correlation
EVENTDATA_DATA_TARGETDOMAINNAME_LCASE is highly correlated with COUNTRY_EVENTDATA_DATA_IPADDRESS and 6 other fieldsHigh correlation
EVENTDATA_DATA_TARGETDOMAINNAME_REVERSE is highly correlated with COUNTRY_EVENTDATA_DATA_IPADDRESS and 6 other fieldsHigh correlation
IPBRANCHCATEGORY_EVENTDATA_DATA_IPADDRESS is highly correlated with COUNTRY_EVENTDATA_DATA_IPADDRESS and 9 other fieldsHigh correlation
LATITUDE_EVENTDATA_DATA_IPADDRESS is highly correlated with COUNTRY_EVENTDATA_DATA_IPADDRESS and 9 other fieldsHigh correlation
LOCATION is highly correlated with COUNTRY_EVENTDATA_DATA_IPADDRESS and 8 other fieldsHigh correlation
DOMAIN is highly correlated with LOCATION and 4 other fieldsHigh correlation
LONGITUDE_EVENTDATA_DATA_IPADDRESS is highly correlated with COUNTRY_EVENTDATA_DATA_IPADDRESS and 9 other fieldsHigh correlation
NETWORK_COLLECTION is highly correlated with DOMAIN and 4 other fieldsHigh correlation
SITE_COLLECTION is highly correlated with COUNTRY_EVENTDATA_DATA_IPADDRESS and 9 other fieldsHigh correlation
DOMAINCONTROLLERNUMBER is highly correlated with SITE_COLLECTION and 2 other fieldsHigh correlation
SYSTEM_COMPUTER is highly correlated with COUNTRY_EVENTDATA_DATA_IPADDRESS and 9 other fieldsHigh correlation
SYSTEM_COMPUTER_REVERSE is highly correlated with COUNTRY_EVENTDATA_DATA_IPADDRESS and 9 other fieldsHigh correlation
ARCHITECTURE has 307183 (9.3%) missing values Missing
DOMAIN has 307183 (9.3%) missing values Missing
DOMAINCONTROLLER has 307183 (9.3%) missing values Missing
DOMAINCONTROLLERNUMBER has 307183 (9.3%) missing values Missing
EVENTDATA_DATA_LMPACKAGENAME has 3249346 (98.6%) missing values Missing
EVENTDATA_DATA_WORKSTATIONNAME has 3249346 (98.6%) missing values Missing
LOCATION has 307183 (9.3%) missing values Missing
EVENTDATA_DATA_TARGETLOGONID is uniformly distributed Uniform
LOCAL_SYSTEM_TIMECREATED_SYSTEMTIME is uniformly distributed Uniform
LOCAL_TIMESTAMP is uniformly distributed Uniform
SYSTEM_TIMECREATED_SYSTEMTIME is uniformly distributed Uniform
Id has unique values Unique

Reproduction

Analysis started2020-10-01 02:07:24.187932
Analysis finished2020-10-01 02:25:54.159137
Duration18 minutes and 29.97 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Id
Categorical

UNIQUE

Distinct3296031
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
9a3c63c20dafa9ac4c235af9d36c19da
 
1
5419a91ab6a4c2684c642f89150db801
 
1
30a8bab155562a90ee7bf59309af8558
 
1
da5106d2c837210e3a800cbb169dd5f9
 
1
04a836845a5d135f467cb57f466f1688
 
1
Other values (3296026)
3296026 
ValueCountFrequency (%) 
9a3c63c20dafa9ac4c235af9d36c19da1< 0.1%
 
5419a91ab6a4c2684c642f89150db8011< 0.1%
 
30a8bab155562a90ee7bf59309af85581< 0.1%
 
da5106d2c837210e3a800cbb169dd5f91< 0.1%
 
04a836845a5d135f467cb57f466f16881< 0.1%
 
91c43bf1430ccfc0854a8594c0c939541< 0.1%
 
31e3d8ed94f79099ce484626b305037a1< 0.1%
 
878c70a06d140b0e5981129d9f588d3c1< 0.1%
 
d4fa8541e9ef7a71894bbab7e310aab51< 0.1%
 
566b9e5806912c88233019135157996f1< 0.1%
 
Other values (3296021)3296021> 99.9%
 
2020-09-30T22:26:16.759258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3296031 ?
Unique (%)100.0%
2020-09-30T22:26:16.919798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length32
Median length32
Mean length32
Min length32

Timestamp
Real number (ℝ≥0)

Distinct3170345
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.600857555e+12
Minimum1.60056e+12
Maximum1.601158981e+12
Zeros0
Zeros (%)0.0%
Memory size25.1 MiB
2020-09-30T22:26:18.958486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.60056e+12
5-th percentile1.600593735e+12
Q11.600714902e+12
median1.600858842e+12
Q31.600995552e+12
95-th percentile1.60112561e+12
Maximum1.601158981e+12
Range598980538
Interquartile range (IQR)280650728

Descriptive statistics

Standard deviation167328535.3
Coefficient of variation (CV)0.0001045243125
Kurtosis-1.124478905
Mean1.600857555e+12
Median Absolute Deviation (MAD)140837553
Skewness0.02346747073
Sum5.276476129e+18
Variance2.799883873e+16
MonotocityNot monotonic
2020-09-30T22:26:19.114041image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.600621202e+1215< 0.1%
 
1.600880402e+1213< 0.1%
 
1.600880401e+1212< 0.1%
 
1.600880401e+1212< 0.1%
 
1.600621202e+1211< 0.1%
 
1.600707602e+1211< 0.1%
 
1.600707602e+1210< 0.1%
 
1.600707601e+1210< 0.1%
 
1.600693202e+1210< 0.1%
 
1.600707601e+1210< 0.1%
 
Other values (3170335)3295917> 99.9%
 
ValueCountFrequency (%) 
1.60056e+121< 0.1%
 
1.60056e+121< 0.1%
 
1.60056e+122< 0.1%
 
1.60056e+121< 0.1%
 
1.60056e+121< 0.1%
 
ValueCountFrequency (%) 
1.601158981e+121< 0.1%
 
1.601158907e+121< 0.1%
 
1.601158859e+121< 0.1%
 
1.601158642e+121< 0.1%
 
1.601158128e+121< 0.1%
 

Data Type
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
evtx-security-c
3296031 
ValueCountFrequency (%) 
evtx-security-c3296031100.0%
 
2020-09-30T22:26:19.288577image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-30T22:26:19.387312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:26:19.474079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length15
Median length15
Mean length15
Min length15

Visibility
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
U&FOUO
3296031 
ValueCountFrequency (%) 
U&FOUO3296031100.0%
 
2020-09-30T22:26:19.608720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-30T22:26:19.703465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:26:19.797242image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length6
Mean length6
Min length6

ARCHITECTURE
Categorical

MISSING

Distinct1
Distinct (%)< 0.1%
Missing307183
Missing (%)9.3%
Memory size25.1 MiB
P
2988848 
ValueCountFrequency (%) 
P298884890.7%
 
(Missing)3071839.3%
 
2020-09-30T22:26:19.930889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-30T22:26:20.018622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:26:20.118355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length1
Mean length1.186395698
Min length1

COCOM_EVENTDATA_DATA_IPADDRESS
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
USINDOPACOM
3296031 
ValueCountFrequency (%) 
USINDOPACOM3296031100.0%
 
2020-09-30T22:26:20.317822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-30T22:26:20.442491image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:26:20.561174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length11
Mean length11
Min length11

COUNTRY_EVENTDATA_DATA_IPADDRESS
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
PR
3296017 
TW
 
14
ValueCountFrequency (%) 
PR3296017> 99.9%
 
TW14< 0.1%
 
2020-09-30T22:26:20.744714image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-30T22:26:20.856384image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:26:20.966090image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

DOMAIN
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)< 0.1%
Missing307183
Missing (%)9.3%
Memory size25.1 MiB
SE
2984842 
NW
 
1261
DS
 
1192
NE
 
1136
SW
 
417
ValueCountFrequency (%) 
SE298484290.6%
 
NW1261< 0.1%
 
DS1192< 0.1%
 
NE1136< 0.1%
 
SW417< 0.1%
 
(Missing)3071839.3%
 
2020-09-30T22:26:21.134637image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-30T22:26:21.260303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:26:21.418879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length2
Mean length2.093197849
Min length2

DOMAINCONTROLLER
Categorical

MISSING

Distinct1
Distinct (%)< 0.1%
Missing307183
Missing (%)9.3%
Memory size25.1 MiB
A1
2988848 
ValueCountFrequency (%) 
A1298884890.7%
 
(Missing)3071839.3%
 
2020-09-30T22:26:21.559501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-30T22:26:21.646270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:26:21.733083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length2
Mean length2.093197849
Min length2

DOMAINCONTROLLERNUMBER
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing307183
Missing (%)9.3%
Memory size25.1 MiB
2
1507410 
1
1479994 
4
 
806
3
 
638
ValueCountFrequency (%) 
2150741045.7%
 
1147999444.9%
 
4806< 0.1%
 
3638< 0.1%
 
(Missing)3071839.3%
 
2020-09-30T22:26:21.890617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-30T22:26:22.009331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:26:22.140978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

EVENTDATA_DATA_AUTHENTICATIONPACKAGENAME
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
Kerberos
3249346 
NTLM
 
46685
ValueCountFrequency (%) 
Kerberos324934698.6%
 
NTLM466851.4%
 
2020-09-30T22:26:22.282568image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-30T22:26:22.387289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:26:22.505006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length8
Mean length7.943343979
Min length4
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
%%1833
3270428 
%%1840
 
25603
ValueCountFrequency (%) 
%%1833327042899.2%
 
%%1840256030.8%
 
2020-09-30T22:26:22.659560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-30T22:26:22.755337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:26:22.853040image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length6
Mean length6
Min length6

EVENTDATA_DATA_IPADDRESS
Categorical

HIGH CARDINALITY

Distinct473
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
206.37.250.242
 
163965
206.37.250.246
 
157570
192.86.234.58
 
29375
192.86.236.54
 
21703
192.86.236.212
 
18586
Other values (468)
2904832 
ValueCountFrequency (%) 
206.37.250.2421639655.0%
 
206.37.250.2461575704.8%
 
192.86.234.58293750.9%
 
192.86.236.54217030.7%
 
192.86.236.212185860.6%
 
192.86.236.12163900.5%
 
192.86.233.249158230.5%
 
192.86.233.58157140.5%
 
192.86.233.28156770.5%
 
192.86.233.77150060.5%
 
Other values (463)282622285.7%
 
2020-09-30T22:26:23.017602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)< 0.1%
2020-09-30T22:26:23.195126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length14
Median length13
Mean length13.48028887
Min length12

EVENTDATA_DATA_IPPORT
Real number (ℝ≥0)

Distinct18656
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57381.07743
Minimum2278
Maximum65534
Zeros0
Zeros (%)0.0%
Memory size25.1 MiB
2020-09-30T22:26:23.361483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2278
5-th percentile49932
Q153296
median57464
Q361524
95-th percentile64736
Maximum65534
Range63256
Interquartile range (IQR)8228

Descriptive statistics

Standard deviation4820.427109
Coefficient of variation (CV)0.08400726033
Kurtosis1.389056667
Mean57381.07743
Median Absolute Deviation (MAD)4111
Skewness-0.2980215218
Sum1.8912981e+11
Variance23236517.52
MonotocityNot monotonic
2020-09-30T22:26:23.506143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
49732264< 0.1%
 
49757263< 0.1%
 
49753262< 0.1%
 
49733261< 0.1%
 
49770261< 0.1%
 
49761260< 0.1%
 
49752259< 0.1%
 
49758257< 0.1%
 
49769257< 0.1%
 
53562256< 0.1%
 
Other values (18646)329343199.9%
 
ValueCountFrequency (%) 
22781< 0.1%
 
22811< 0.1%
 
22821< 0.1%
 
22901< 0.1%
 
22911< 0.1%
 
ValueCountFrequency (%) 
65534201< 0.1%
 
65533211< 0.1%
 
65532209< 0.1%
 
65531204< 0.1%
 
65530205< 0.1%
 

EVENTDATA_DATA_KEYLENGTH
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
0
3249346 
128
 
46685
ValueCountFrequency (%) 
0324934698.6%
 
128466851.4%
 
2020-09-30T22:26:23.670660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-30T22:26:24.275101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:26:24.404752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length1
Mean length1.02832801
Min length1

EVENTDATA_DATA_LMPACKAGENAME
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing3249346
Missing (%)98.6%
Memory size25.1 MiB
NTLM V2
46072 
NTLM V1
 
613
ValueCountFrequency (%) 
NTLM V2460721.4%
 
NTLM V1613< 0.1%
 
(Missing)324934698.6%
 
2020-09-30T22:26:24.575296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-30T22:26:24.670043image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:26:24.785733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length3
Mean length3.056656021
Min length3

EVENTDATA_DATA_LOGONGUID
Categorical

HIGH CARDINALITY

Distinct126003
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
00000000-0000-0000-0000-000000000000
 
46685
081618EB-6120-AC92-E84E-15978B1BAC2B
 
6194
033022EA-62BE-8B5A-21F0-914BCCF31EFA
 
6187
DC3CEB97-FEA6-5E0C-F9ED-DD42AEB290C3
 
5804
F3C23B1C-93BF-41F8-5EEE-0E9CDB7F0E64
 
5796
Other values (125998)
3225365 
ValueCountFrequency (%) 
00000000-0000-0000-0000-000000000000466851.4%
 
081618EB-6120-AC92-E84E-15978B1BAC2B61940.2%
 
033022EA-62BE-8B5A-21F0-914BCCF31EFA61870.2%
 
DC3CEB97-FEA6-5E0C-F9ED-DD42AEB290C358040.2%
 
F3C23B1C-93BF-41F8-5EEE-0E9CDB7F0E6457960.2%
 
EF28C111-190A-F9B3-90FD-7654D73AD4D757690.2%
 
EA8A17F3-838A-2810-B5C0-F0E730F7D90657470.2%
 
E2B60118-DFB4-9687-CFB7-00B8044EC70757390.2%
 
614E7F26-F443-129D-1A87-A641512D2CC557270.2%
 
A7341535-E133-57EF-0B01-A3D0BD3D411756920.2%
 
Other values (125993)319669197.0%
 
2020-09-30T22:26:25.574623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique39110 ?
Unique (%)1.2%
2020-09-30T22:26:25.748159image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length36
Median length36
Mean length36
Min length36

EVENTDATA_DATA_LOGONPROCESSNAME
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
Kerberos
3249346 
NtLmSsp
 
46685
ValueCountFrequency (%) 
Kerberos324934698.6%
 
NtLmSsp 466851.4%
 
2020-09-30T22:26:25.913716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-30T22:26:26.038386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:26:26.164046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length8
Mean length8
Min length8

EVENTDATA_DATA_LOGONTYPE
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
3
3296031 
ValueCountFrequency (%) 
33296031100.0%
 
2020-09-30T22:26:26.312649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-30T22:26:26.411413image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:26:26.512122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

EVENTDATA_DATA_PROCESSID
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
0x0
3296031 
ValueCountFrequency (%) 
0x03296031100.0%
 
2020-09-30T22:26:26.672154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-30T22:26:26.768914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:26:26.859685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

EVENTDATA_DATA_SUBJECTLOGONID
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
0x0
3296031 
ValueCountFrequency (%) 
0x03296031100.0%
 
2020-09-30T22:26:27.000309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-30T22:26:27.097090image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:26:27.191807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

EVENTDATA_DATA_SUBJECTUSERSID
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
S-1-0-0
3296031 
ValueCountFrequency (%) 
S-1-0-03296031100.0%
 
2020-09-30T22:26:27.327441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-30T22:26:27.423187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:26:27.526942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length7
Mean length7
Min length7

EVENTDATA_DATA_TARGETDOMAINNAME
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
NASE
3295404 
NT AUTHORITY
 
613
NANW
 
13
NAE
 
1
ValueCountFrequency (%) 
NASE3295404> 99.9%
 
NT AUTHORITY613< 0.1%
 
NANW13< 0.1%
 
NAE1< 0.1%
 
2020-09-30T22:26:27.694464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-09-30T22:26:27.802207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:26:27.940848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length12
Median length4
Mean length4.001487547
Min length3

EVENTDATA_DATA_TARGETDOMAINNAME_LCASE
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
nase
3295404 
nt authority
 
613
nanw
 
13
nae
 
1
ValueCountFrequency (%) 
nase3295404> 99.9%
 
nt authority613< 0.1%
 
nanw13< 0.1%
 
nae1< 0.1%
 
2020-09-30T22:26:28.091478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-09-30T22:26:28.208133image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:26:28.342773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length12
Median length4
Mean length4.001487547
Min length3

EVENTDATA_DATA_TARGETDOMAINNAME_REVERSE
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
NASE
3295404 
NT AUTHORITY
 
613
NANW
 
13
NAE
 
1
ValueCountFrequency (%) 
NASE3295404> 99.9%
 
NT AUTHORITY613< 0.1%
 
NANW13< 0.1%
 
NAE1< 0.1%
 
2020-09-30T22:26:28.489381image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-09-30T22:26:28.600087image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:26:28.747697image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length12
Median length4
Mean length4.001487547
Min length3

EVENTDATA_DATA_TARGETLOGONID
Categorical

HIGH CARDINALITY
UNIFORM

Distinct3295757
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
0x125bcd5d8
 
2
0xcf350a1a
 
2
0x72ca1632
 
2
0x2798646e
 
2
0x120887a86
 
2
Other values (3295752)
3296021 
ValueCountFrequency (%) 
0x125bcd5d82< 0.1%
 
0xcf350a1a2< 0.1%
 
0x72ca16322< 0.1%
 
0x2798646e2< 0.1%
 
0x120887a862< 0.1%
 
0x10ff093762< 0.1%
 
0x9a3cbe412< 0.1%
 
0xd1be5a5b2< 0.1%
 
0xd18736332< 0.1%
 
0xd03a76bd2< 0.1%
 
Other values (3295747)3296011> 99.9%
 
2020-09-30T22:26:49.270584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3295483 ?
Unique (%)> 99.9%
2020-09-30T22:26:49.430191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length10
Mean length10.21045767
Min length8

EVENTDATA_DATA_TARGETUSERNAME
Categorical

HIGH CARDINALITY

Distinct984
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
BUCHA1SEP000001$
 
163953
BUCHA1SEP000002$
 
157563
carlos.nievesbadillo
 
22240
nancy.ramos1
 
15467
BUCHA7FSGP01$
 
13876
Other values (979)
2922932 
ValueCountFrequency (%) 
BUCHA1SEP000001$1639535.0%
 
BUCHA1SEP000002$1575634.8%
 
carlos.nievesbadillo222400.7%
 
nancy.ramos1154670.5%
 
BUCHA7FSGP01$138760.4%
 
christopher.c.nelson112060.3%
 
BUCHW1H1AAWK251$105360.3%
 
BUCHB4RADIUS01$103630.3%
 
BUCHW1H1AAWK247$99150.3%
 
BUCHW1H1AAWK245$94620.3%
 
Other values (974)287145087.1%
 
2020-09-30T22:26:49.598711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)< 0.1%
2020-09-30T22:26:49.760274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length20
Median length16
Mean length15.95537845
Min length7

EVENTDATA_DATA_TARGETUSERNAME_LCASE
Categorical

HIGH CARDINALITY

Distinct984
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
bucha1sep000001$
 
163953
bucha1sep000002$
 
157563
carlos.nievesbadillo
 
22240
nancy.ramos1
 
15467
bucha7fsgp01$
 
13876
Other values (979)
2922932 
ValueCountFrequency (%) 
bucha1sep000001$1639535.0%
 
bucha1sep000002$1575634.8%
 
carlos.nievesbadillo222400.7%
 
nancy.ramos1154670.5%
 
bucha7fsgp01$138760.4%
 
christopher.c.nelson112060.3%
 
buchw1h1aawk251$105360.3%
 
buchb4radius01$103630.3%
 
buchw1h1aawk247$99150.3%
 
buchw1h1aawk245$94620.3%
 
Other values (974)287145087.1%
 
2020-09-30T22:26:49.932813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)< 0.1%
2020-09-30T22:26:50.129288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length20
Median length16
Mean length15.95537845
Min length7

EVENTDATA_DATA_TARGETUSERSID
Categorical

HIGH CARDINALITY

Distinct1059
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
S-1-5-18
 
211712
S-1-5-21-3822721094-983390456-1902330015-10755300
 
57042
S-1-5-21-3822721094-983390456-1902330015-10484437
 
52762
S-1-5-21-3822721094-983390456-1902330015-11492077
 
22240
S-1-5-21-3822721094-983390456-1902330015-352989
 
15467
Other values (1054)
2936808 
ValueCountFrequency (%) 
S-1-5-182117126.4%
 
S-1-5-21-3822721094-983390456-1902330015-10755300570421.7%
 
S-1-5-21-3822721094-983390456-1902330015-10484437527621.6%
 
S-1-5-21-3822721094-983390456-1902330015-11492077222400.7%
 
S-1-5-21-3822721094-983390456-1902330015-352989154670.5%
 
S-1-5-21-3822721094-983390456-1902330015-10172758138760.4%
 
S-1-5-21-3822721094-983390456-1902330015-214680112060.3%
 
S-1-5-21-3822721094-983390456-1902330015-17116019105360.3%
 
S-1-5-21-3822721094-983390456-1902330015-8147679103630.3%
 
S-1-5-21-3822721094-983390456-1902330015-1644024099150.3%
 
Other values (1049)288091287.4%
 
2020-09-30T22:26:50.383608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)< 0.1%
2020-09-30T22:26:50.595042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length50
Median length49
Mean length46.09201127
Min length7

EVENTDATA_DATA_WORKSTATIONNAME
Categorical

HIGH CARDINALITY
MISSING

Distinct264
Distinct (%)0.6%
Missing3249346
Missing (%)98.6%
Memory size25.1 MiB
BUCHA1SEP000001
16399 
BUCHA1SEP000002
13211 
BUCHA7FSGP01
11220 
BUCHPSPR2
 
1065
BUCHW6SAAAWK018
 
720
Other values (259)
4070 
ValueCountFrequency (%) 
BUCHA1SEP000001163990.5%
 
BUCHA1SEP000002132110.4%
 
BUCHA7FSGP01112200.3%
 
BUCHPSPR21065< 0.1%
 
BUCHW6SAAAWK018720< 0.1%
 
BUCHA0NACS02556< 0.1%
 
BUCHA0NACS01517< 0.1%
 
BUCHNETCB6NS001261< 0.1%
 
BUCHA0SWSYSLOG212< 0.1%
 
BUCHA7WDS209< 0.1%
 
Other values (254)23150.1%
 
(Missing)324934698.6%
 
2020-09-30T22:26:50.803485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique95 ?
Unique (%)0.2%
2020-09-30T22:26:50.981009image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length15
Median length3
Mean length3.155030095
Min length3

FILENAME_INGEST
Categorical

HIGH CARDINALITY

Distinct3881
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
nase.ds.army.mil_bucha1sep000001_security_2020_09_25_bucha1sep000001-security-2020-09-25-20-32-57-736.xml.zip
 
99744
nase.ds.army.mil_bucha1sep000002_security_2020_09_23_bucha1sep000002-security-2020-09-23-19-16-22-492.xml.zip
 
98295
nase.ds.army.mil_bucha1sep000001_security_2020_09_26_bucha1sep000001-security-2020-09-26-09-55-11-528.xml.zip,nase.ds.army.mil_bucha1sep000001_security_2020_09_26_bucha1sep000001-security-2020-09-26-12-06-42-977.xml.zip
 
95460
nase.ds.army.mil_bucha1sep000002_security_2020_09_23_bucha1sep000002-security-2020-09-23-07-37-50-904.xml.zip
 
88647
nase.ds.army.mil_bucha1sep000001_security_2020_09_23_bucha1sep000001-security-2020-09-23-21-55-11-736.xml.zip,nase.ds.army.mil_bucha1sep000001_security_2020_09_24_bucha1sep000001-security-2020-09-24-00-35-15-765.xml.zip
 
87291
Other values (3876)
2826594 
ValueCountFrequency (%) 
nase.ds.army.mil_bucha1sep000001_security_2020_09_25_bucha1sep000001-security-2020-09-25-20-32-57-736.xml.zip997443.0%
 
nase.ds.army.mil_bucha1sep000002_security_2020_09_23_bucha1sep000002-security-2020-09-23-19-16-22-492.xml.zip982953.0%
 
nase.ds.army.mil_bucha1sep000001_security_2020_09_26_bucha1sep000001-security-2020-09-26-09-55-11-528.xml.zip,nase.ds.army.mil_bucha1sep000001_security_2020_09_26_bucha1sep000001-security-2020-09-26-12-06-42-977.xml.zip954602.9%
 
nase.ds.army.mil_bucha1sep000002_security_2020_09_23_bucha1sep000002-security-2020-09-23-07-37-50-904.xml.zip886472.7%
 
nase.ds.army.mil_bucha1sep000001_security_2020_09_23_bucha1sep000001-security-2020-09-23-21-55-11-736.xml.zip,nase.ds.army.mil_bucha1sep000001_security_2020_09_24_bucha1sep000001-security-2020-09-24-00-35-15-765.xml.zip872912.6%
 
nase.ds.army.mil_bucha1sep000001_security_2020_09_25_bucha1sep000001-security-2020-09-25-09-55-11-691.xml.zip,nase.ds.army.mil_bucha1sep000001_security_2020_09_25_bucha1sep000001-security-2020-09-25-10-04-23-670.xml.zip859372.6%
 
nase.ds.army.mil_bucha1sep000001_security_2020_09_26_bucha1sep000001-security-2020-09-26-21-55-11-965.xml.zip848022.6%
 
nase.ds.army.mil_bucha1sep000002_security_2020_09_24_bucha1sep000002-security-2020-09-24-07-51-10-541.xml.zip836012.5%
 
nase.ds.army.mil_bucha1sep000002_security_2020_09_21_bucha1sep000002-security-2020-09-21-21-55-11-897.xml.zip,nase.ds.army.mil_bucha1sep000002_security_2020_09_22_bucha1sep000002-security-2020-09-22-02-17-11-797.xml.zip809362.5%
 
nase.ds.army.mil_bucha1sep000001_security_2020_09_24_bucha1sep000001-security-2020-09-24-09-55-11-549.xml.zip,nase.ds.army.mil_bucha1sep000001_security_2020_09_24_bucha1sep000001-security-2020-09-24-13-41-49-548.xml.zip796592.4%
 
Other values (3871)241165973.2%
 
2020-09-30T22:26:51.192444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique556 ?
Unique (%)< 0.1%
2020-09-30T22:26:51.376985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length338
Median length109
Mean length157.8214741
Min length104
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
Army
3296017 
Non-Army
 
14
ValueCountFrequency (%) 
Army3296017> 99.9%
 
Non-Army14< 0.1%
 
2020-09-30T22:26:51.534559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-30T22:26:51.637256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:26:51.750952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length4
Mean length4.00001699
Min length4

LATITUDE_EVENTDATA_DATA_IPADDRESS
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
18.411313
3296017 
23.5
 
14
ValueCountFrequency (%) 
18.4113133296017> 99.9%
 
23.514< 0.1%
 
2020-09-30T22:26:51.924559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-30T22:26:52.022297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:26:52.130010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length9
Mean length8.999978762
Min length4

LOCAL_SYSTEM_TIMECREATED_SYSTEMTIME
Categorical

HIGH CARDINALITY
UNIFORM

Distinct3170345
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
2020-09-20T13:00:01.562-04:00[America/Puerto_Rico]
 
15
2020-09-23T13:00:01.915-04:00[America/Puerto_Rico]
 
13
2020-09-23T13:00:01.290-04:00[America/Puerto_Rico]
 
12
2020-09-23T13:00:01.087-04:00[America/Puerto_Rico]
 
12
2020-09-21T13:00:01.587-04:00[America/Puerto_Rico]
 
11
Other values (3170340)
3295968 
ValueCountFrequency (%) 
2020-09-20T13:00:01.562-04:00[America/Puerto_Rico]15< 0.1%
 
2020-09-23T13:00:01.915-04:00[America/Puerto_Rico]13< 0.1%
 
2020-09-23T13:00:01.290-04:00[America/Puerto_Rico]12< 0.1%
 
2020-09-23T13:00:01.087-04:00[America/Puerto_Rico]12< 0.1%
 
2020-09-21T13:00:01.587-04:00[America/Puerto_Rico]11< 0.1%
 
2020-09-20T13:00:01.750-04:00[America/Puerto_Rico]11< 0.1%
 
2020-09-20T13:00:01.531-04:00[America/Puerto_Rico]10< 0.1%
 
2020-09-21T09:00:01.535-04:00[America/Puerto_Rico]10< 0.1%
 
2020-09-21T13:00:01.009-04:00[America/Puerto_Rico]10< 0.1%
 
2020-09-21T13:00:01.556-04:00[America/Puerto_Rico]10< 0.1%
 
Other values (3170335)3295917> 99.9%
 
2020-09-30T22:27:14.526174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3057810 ?
Unique (%)92.8%
2020-09-30T22:27:14.782456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length50
Median length50
Mean length49.99581345
Min length42

LOCAL_TIMESTAMP
Categorical

HIGH CARDINALITY
UNIFORM

Distinct3170345
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
2020-09-20T13:00:01.562-04:00[America/Puerto_Rico]
 
15
2020-09-23T13:00:01.915-04:00[America/Puerto_Rico]
 
13
2020-09-23T13:00:01.290-04:00[America/Puerto_Rico]
 
12
2020-09-23T13:00:01.087-04:00[America/Puerto_Rico]
 
12
2020-09-21T13:00:01.587-04:00[America/Puerto_Rico]
 
11
Other values (3170340)
3295968 
ValueCountFrequency (%) 
2020-09-20T13:00:01.562-04:00[America/Puerto_Rico]15< 0.1%
 
2020-09-23T13:00:01.915-04:00[America/Puerto_Rico]13< 0.1%
 
2020-09-23T13:00:01.290-04:00[America/Puerto_Rico]12< 0.1%
 
2020-09-23T13:00:01.087-04:00[America/Puerto_Rico]12< 0.1%
 
2020-09-21T13:00:01.587-04:00[America/Puerto_Rico]11< 0.1%
 
2020-09-20T13:00:01.750-04:00[America/Puerto_Rico]11< 0.1%
 
2020-09-20T13:00:01.531-04:00[America/Puerto_Rico]10< 0.1%
 
2020-09-21T09:00:01.535-04:00[America/Puerto_Rico]10< 0.1%
 
2020-09-21T13:00:01.009-04:00[America/Puerto_Rico]10< 0.1%
 
2020-09-21T13:00:01.556-04:00[America/Puerto_Rico]10< 0.1%
 
Other values (3170335)3295917> 99.9%
 
2020-09-30T22:27:38.248502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3057810 ?
Unique (%)92.8%
2020-09-30T22:27:38.406084image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length50
Median length50
Mean length49.99581345
Min length42

LOCATION
Categorical

HIGH CORRELATION
MISSING

Distinct15
Distinct (%)< 0.1%
Missing307183
Missing (%)9.3%
Memory size25.1 MiB
BUCH
2915466 
RUCK
 
52245
BLUE
 
17131
RILE
 
1247
HUAC
 
1047
Other values (10)
 
1712
ValueCountFrequency (%) 
BUCH291546688.5%
 
RUCK522451.6%
 
BLUE171310.5%
 
RILE1247< 0.1%
 
HUAC1047< 0.1%
 
BELV373< 0.1%
 
MYER304< 0.1%
 
HAMI280< 0.1%
 
REDS248< 0.1%
 
SCOT161< 0.1%
 
Other values (5)346< 0.1%
 
(Missing)3071839.3%
 
2020-09-30T22:27:38.578620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-09-30T22:27:38.742184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length3.906802151
Min length3

LONGITUDE_EVENTDATA_DATA_IPADDRESS
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
-66.124234
3296017 
121
 
14
ValueCountFrequency (%) 
-66.1242343296017> 99.9%
 
12114< 0.1%
 
2020-09-30T22:27:38.909767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-30T22:27:39.022433image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:27:39.156079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length9.999978762
Min length5

NETWORK_COLLECTION
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
nase.ds.army.mil
3145619 
ds.army.mil
 
79329
nanw.ds.army.mil
 
20774
dahq.ds.army.mil
 
20168
nae.ds.army.mil
 
18519
Other values (2)
 
11622
ValueCountFrequency (%) 
nase.ds.army.mil314561995.4%
 
ds.army.mil793292.4%
 
nanw.ds.army.mil207740.6%
 
dahq.ds.army.mil201680.6%
 
nae.ds.army.mil185190.6%
 
nasw.ds.army.mil70610.2%
 
service.ds.army.mil45610.1%
 
2020-09-30T22:27:39.658731image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-30T22:27:39.757466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:27:39.948957image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length16
Mean length15.87819259
Min length11
Distinct1
Distinct (%)< 0.1%
Missing14
Missing (%)< 0.1%
Memory size25.1 MiB
FORT BUCHANAN
3296017 
ValueCountFrequency (%) 
FORT BUCHANAN3296017> 99.9%
 
(Missing)14< 0.1%
 
2020-09-30T22:27:40.094565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-30T22:27:40.202278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:27:40.302010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length13
Mean length12.99995752
Min length3

SITE_COLLECTION
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct97
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
bucha1sep000002
1464328 
bucha1sep000001
1451138 
bragw4nhaah0se2
 
39845
bragw4nhaah0se1
 
38109
carsa1sevxd0002
 
35974
Other values (92)
266637 
ValueCountFrequency (%) 
bucha1sep000002146432844.4%
 
bucha1sep000001145113844.0%
 
bragw4nhaah0se2398451.2%
 
bragw4nhaah0se1381091.2%
 
carsa1sevxd0002359741.1%
 
carsa1sevxd0001350671.1%
 
braga1dsvxd0002349891.1%
 
rucka1sep000001276440.8%
 
braga1dsvxd0001274790.8%
 
rucka1sep000002246010.7%
 
Other values (87)1168573.5%
 
2020-09-30T22:27:40.507461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)< 0.1%
2020-09-30T22:27:40.698949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length15
Median length15
Mean length15
Min length15

SYSTEM_CHANNEL
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
Security
3296031 
ValueCountFrequency (%) 
Security3296031100.0%
 
2020-09-30T22:27:40.838575image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-30T22:27:40.930329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:27:41.018095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length8
Mean length8
Min length8

SYSTEM_COMPUTER
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct97
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
BUCHA1SEP000002.nase.ds.army.mil
1464328 
BUCHA1SEP000001.nase.ds.army.mil
1451138 
BRAGW4NHAAH0SE2.nase.ds.army.mil
 
39845
BRAGW4NHAAH0SE1.nase.ds.army.mil
 
38109
CARSA1SEVXD0002.nase.ds.army.mil
 
35974
Other values (92)
266637 
ValueCountFrequency (%) 
BUCHA1SEP000002.nase.ds.army.mil146432844.4%
 
BUCHA1SEP000001.nase.ds.army.mil145113844.0%
 
BRAGW4NHAAH0SE2.nase.ds.army.mil398451.2%
 
BRAGW4NHAAH0SE1.nase.ds.army.mil381091.2%
 
CARSA1SEVXD0002.nase.ds.army.mil359741.1%
 
CARSA1SEVXD0001.nase.ds.army.mil350671.1%
 
BRAGA1DSVXD0002.ds.army.mil349891.1%
 
RUCKA1SEP000001.nase.ds.army.mil276440.8%
 
BRAGA1DSVXD0001.ds.army.mil274790.8%
 
RUCKA1SEP000002.nase.ds.army.mil246010.7%
 
Other values (87)1168573.5%
 
2020-09-30T22:27:41.186644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)< 0.1%
2020-09-30T22:27:41.363172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length35
Median length32
Mean length31.87819714
Min length27

SYSTEM_COMPUTER_REVERSE
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct97
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
mil.army.ds.nase.BUCHA1SEP000002
1464328 
mil.army.ds.nase.BUCHA1SEP000001
1451138 
mil.army.ds.nase.BRAGW4NHAAH0SE2
 
39845
mil.army.ds.nase.BRAGW4NHAAH0SE1
 
38109
mil.army.ds.nase.CARSA1SEVXD0002
 
35974
Other values (92)
266637 
ValueCountFrequency (%) 
mil.army.ds.nase.BUCHA1SEP000002146432844.4%
 
mil.army.ds.nase.BUCHA1SEP000001145113844.0%
 
mil.army.ds.nase.BRAGW4NHAAH0SE2398451.2%
 
mil.army.ds.nase.BRAGW4NHAAH0SE1381091.2%
 
mil.army.ds.nase.CARSA1SEVXD0002359741.1%
 
mil.army.ds.nase.CARSA1SEVXD0001350671.1%
 
mil.army.ds.BRAGA1DSVXD0002349891.1%
 
mil.army.ds.nase.RUCKA1SEP000001276440.8%
 
mil.army.ds.BRAGA1DSVXD0001274790.8%
 
mil.army.ds.nase.RUCKA1SEP000002246010.7%
 
Other values (87)1168573.5%
 
2020-09-30T22:27:41.539732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)< 0.1%
2020-09-30T22:27:41.708250image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length35
Median length32
Mean length31.87819714
Min length27

SYSTEM_EVENTID
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
4624
3296031 
ValueCountFrequency (%) 
46243296031100.0%
 
2020-09-30T22:27:41.848900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-30T22:27:41.929657image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:27:42.009443image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length4
Min length4

SYSTEM_EVENTRECORDID
Real number (ℝ≥0)

Distinct3295762
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1033912897
Minimum25970053
Maximum1.317018726e+10
Zeros0
Zeros (%)0.0%
Memory size25.1 MiB
2020-09-30T22:27:44.019069image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum25970053
5-th percentile715501397
Q1717807622.5
median755951384
Q3758796238
95-th percentile1783433106
Maximum1.317018726e+10
Range1.31442172e+10
Interquartile range (IQR)40988615.5

Descriptive statistics

Standard deviation1605470878
Coefficient of variation (CV)1.552810572
Kurtosis45.28173914
Mean1033912897
Median Absolute Deviation (MAD)36196759
Skewness6.631974294
Sum3.407808961e+15
Variance2.57753674e+18
MonotocityNot monotonic
2020-09-30T22:27:44.238514image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
7164921742< 0.1%
 
7196484932< 0.1%
 
7191543122< 0.1%
 
7179807312< 0.1%
 
7184255232< 0.1%
 
7183952982< 0.1%
 
7165406972< 0.1%
 
7177019912< 0.1%
 
7178532032< 0.1%
 
7162218842< 0.1%
 
Other values (3295752)3296011> 99.9%
 
ValueCountFrequency (%) 
259700531< 0.1%
 
259700691< 0.1%
 
259700731< 0.1%
 
259710221< 0.1%
 
259720171< 0.1%
 
ValueCountFrequency (%) 
1.317018726e+101< 0.1%
 
1.317018132e+101< 0.1%
 
1.317018023e+101< 0.1%
 
1.317017084e+101< 0.1%
 
1.317016703e+101< 0.1%
 

SYSTEM_EXECUTION_PROCESSID
Real number (ℝ≥0)

Distinct54
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean910.7936995
Minimum476
Maximum952
Zeros0
Zeros (%)0.0%
Memory size25.1 MiB
2020-09-30T22:27:44.439942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum476
5-th percentile772
Q1920
median920
Q3932
95-th percentile932
Maximum952
Range476
Interquartile range (IQR)12

Descriptive statistics

Standard deviation49.42732491
Coefficient of variation (CV)0.05426840891
Kurtosis9.987728184
Mean910.7936995
Median Absolute Deviation (MAD)12
Skewness-3.287830579
Sum3002004268
Variance2443.060448
MonotocityNot monotonic
2020-09-30T22:27:44.643399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
920148183245.0%
 
932145113844.0%
 
772771152.3%
 
776399211.2%
 
876388121.2%
 
696381091.2%
 
880310400.9%
 
712296710.9%
 
864246970.7%
 
676124060.4%
 
Other values (44)712902.2%
 
ValueCountFrequency (%) 
4761< 0.1%
 
66834610.1%
 
676124060.4%
 
68032900.1%
 
684759< 0.1%
 
ValueCountFrequency (%) 
95252< 0.1%
 
94062< 0.1%
 
93614< 0.1%
 
932145113844.0%
 
92831980.1%
 

SYSTEM_EXECUTION_THREADID
Real number (ℝ≥0)

Distinct5698
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10120.25315
Minimum32
Maximum24560
Zeros0
Zeros (%)0.0%
Memory size25.1 MiB
2020-09-30T22:27:44.853843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile1644
Q16852
median10820
Q313420
95-th percentile18168
Maximum24560
Range24528
Interquartile range (IQR)6568

Descriptive statistics

Standard deviation4809.018705
Coefficient of variation (CV)0.4751875902
Kurtosis-0.3795239002
Mean10120.25315
Median Absolute Deviation (MAD)2808
Skewness-0.1791781929
Sum3.33566681e+10
Variance23126660.9
MonotocityNot monotonic
2020-09-30T22:27:45.058289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
132081641675.0%
 
136281416964.3%
 
16441279603.9%
 
131161274413.9%
 
91881207103.7%
 
146681150783.5%
 
18641142083.5%
 
94881097183.3%
 
4372940742.9%
 
13420937962.8%
 
Other values (5688)208718363.3%
 
ValueCountFrequency (%) 
3215< 0.1%
 
3678< 0.1%
 
529< 0.1%
 
601< 0.1%
 
645< 0.1%
 
ValueCountFrequency (%) 
245603< 0.1%
 
245481< 0.1%
 
245283< 0.1%
 
2452082< 0.1%
 
245081< 0.1%
 

SYSTEM_KEYWORDS
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
0x8020000000000000
3296031 
ValueCountFrequency (%) 
0x80200000000000003296031100.0%
 
2020-09-30T22:27:45.337544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-30T22:27:45.482155image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:27:45.631756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length18
Median length18
Mean length18
Min length18

SYSTEM_LEVEL
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
0
3296031 
ValueCountFrequency (%) 
03296031100.0%
 
2020-09-30T22:27:45.775371image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

SYSTEM_OPCODE
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
0
3296031 
ValueCountFrequency (%) 
03296031100.0%
 
2020-09-30T22:27:45.834213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

SYSTEM_PROVIDER_GUID
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
54849625-5478-4994-A5BA-3E3B0328C30D
3296031 
ValueCountFrequency (%) 
54849625-5478-4994-A5BA-3E3B0328C30D3296031100.0%
 
2020-09-30T22:27:45.945916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-30T22:27:46.058647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:27:46.176298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length36
Median length36
Mean length36
Min length36

SYSTEM_PROVIDER_NAME
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
Microsoft-Windows-Security-Auditing
3296031 
ValueCountFrequency (%) 
Microsoft-Windows-Security-Auditing3296031100.0%
 
2020-09-30T22:27:46.348837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-30T22:27:46.462566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:27:46.573236image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length35
Median length35
Mean length35
Min length35

SYSTEM_TASK
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
12544
3296031 
ValueCountFrequency (%) 
125443296031100.0%
 
2020-09-30T22:27:46.737797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-30T22:27:46.850495image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:27:46.957210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length5
Mean length5
Min length5

SYSTEM_TIMECREATED_SYSTEMTIME
Categorical

HIGH CARDINALITY
UNIFORM

Distinct3170345
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
2020-09-20T17:00:01.562Z
 
15
2020-09-23T17:00:01.915Z
 
13
2020-09-23T17:00:01.087Z
 
12
2020-09-23T17:00:01.29Z
 
12
2020-09-20T17:00:01.75Z
 
11
Other values (3170340)
3295968 
ValueCountFrequency (%) 
2020-09-20T17:00:01.562Z15< 0.1%
 
2020-09-23T17:00:01.915Z13< 0.1%
 
2020-09-23T17:00:01.087Z12< 0.1%
 
2020-09-23T17:00:01.29Z12< 0.1%
 
2020-09-20T17:00:01.75Z11< 0.1%
 
2020-09-21T17:00:01.587Z11< 0.1%
 
2020-09-21T17:00:01.556Z10< 0.1%
 
2020-09-21T17:00:01.009Z10< 0.1%
 
2020-09-20T17:00:01.531Z10< 0.1%
 
2020-09-21T13:00:01.535Z10< 0.1%
 
Other values (3170335)3295917> 99.9%
 
2020-09-30T22:28:11.770874image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3057810 ?
Unique (%)92.8%
2020-09-30T22:28:11.997237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length24
Median length24
Mean length23.88784177
Min length20

SYSTEM_VERSION
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
1
3296031 
ValueCountFrequency (%) 
13296031100.0%
 
2020-09-30T22:28:12.136894image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Interactions

2020-09-30T22:21:47.835593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:21:50.867510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:21:53.618734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:21:56.623902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:21:59.320688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:22:02.718971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:22:05.182206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:22:07.516328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:22:09.962379image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:22:12.591554image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:22:15.075497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:22:19.800215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:22:24.364745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:22:27.625062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:22:30.073465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:22:32.526902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:22:34.485180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:22:36.329275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:22:38.016730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:22:39.954548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:22:42.411445image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:22:44.891831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:22:46.876521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:22:48.522120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:22:50.274434image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-09-30T22:28:12.257540image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-09-30T22:28:12.720334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-09-30T22:28:13.196029image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-09-30T22:28:13.705665image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-09-30T22:28:14.353966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-09-30T22:23:32.858760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:24:03.247805image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:25:07.639034image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-30T22:25:22.002642image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

IdTimestampData TypeVisibilityARCHITECTURECOCOM_EVENTDATA_DATA_IPADDRESSCOUNTRY_EVENTDATA_DATA_IPADDRESSDOMAINDOMAINCONTROLLERDOMAINCONTROLLERNUMBEREVENTDATA_DATA_AUTHENTICATIONPACKAGENAMEEVENTDATA_DATA_IMPERSONATIONLEVELEVENTDATA_DATA_IPADDRESSEVENTDATA_DATA_IPPORTEVENTDATA_DATA_KEYLENGTHEVENTDATA_DATA_LMPACKAGENAMEEVENTDATA_DATA_LOGONGUIDEVENTDATA_DATA_LOGONPROCESSNAMEEVENTDATA_DATA_LOGONTYPEEVENTDATA_DATA_PROCESSIDEVENTDATA_DATA_SUBJECTLOGONIDEVENTDATA_DATA_SUBJECTUSERSIDEVENTDATA_DATA_TARGETDOMAINNAMEEVENTDATA_DATA_TARGETDOMAINNAME_LCASEEVENTDATA_DATA_TARGETDOMAINNAME_REVERSEEVENTDATA_DATA_TARGETLOGONIDEVENTDATA_DATA_TARGETUSERNAMEEVENTDATA_DATA_TARGETUSERNAME_LCASEEVENTDATA_DATA_TARGETUSERSIDEVENTDATA_DATA_WORKSTATIONNAMEFILENAME_INGESTIPBRANCHCATEGORY_EVENTDATA_DATA_IPADDRESSLATITUDE_EVENTDATA_DATA_IPADDRESSLOCAL_SYSTEM_TIMECREATED_SYSTEMTIMELOCAL_TIMESTAMPLOCATIONLONGITUDE_EVENTDATA_DATA_IPADDRESSNETWORK_COLLECTIONORGANIZATION_OWNER_EVENTDATA_DATA_IPADDRESSSITE_COLLECTIONSYSTEM_CHANNELSYSTEM_COMPUTERSYSTEM_COMPUTER_REVERSESYSTEM_EVENTIDSYSTEM_EVENTRECORDIDSYSTEM_EXECUTION_PROCESSIDSYSTEM_EXECUTION_THREADIDSYSTEM_KEYWORDSSYSTEM_LEVELSYSTEM_OPCODESYSTEM_PROVIDER_GUIDSYSTEM_PROVIDER_NAMESYSTEM_TASKSYSTEM_TIMECREATED_SYSTEMTIMESYSTEM_VERSION
00001bf562bc9b3f9cd1e08d831c07b591600568918548evtx-security-cU&FOUOPUSINDOPACOMPRSEA11.0Kerberos%%1840206.37.251.193516300NaNF21C8E5A-C35E-900F-BBD5-67D58E8DF4E3Kerberos30x00x0S-1-0-0NASEnaseNASE0xb82abb7eBUCHA5C1SRV02$bucha5c1srv02$S-1-5-21-3822721094-983390456-1902330015-9880145NaNnase.ds.army.mil_bucha1sep000001_security_2020_09_20_bucha1sep000001-security-2020-09-20-06-12-16-081.xml.zipArmy18.4113132020-09-19T22:28:38.548-04:00[America/Puerto_Rico]2020-09-19T22:28:38.548-04:00[America/Puerto_Rico]BUCH-66.124234nase.ds.army.milFORT BUCHANANbucha1sep000001SecurityBUCHA1SEP000001.nase.ds.army.milmil.army.ds.nase.BUCHA1SEP000001462475579061893299640x80200000000000000054849625-5478-4994-A5BA-3E3B0328C30DMicrosoft-Windows-Security-Auditing125442020-09-20T02:28:38.548Z1
10018f78e24faa2d33a5edb25389d82a51600631874001evtx-security-cU&FOUOPUSINDOPACOMPRSEA12.0Kerberos%%1833192.86.237.130613200NaNA4916DF8-6F24-0D55-B077-26360B90ED7AKerberos30x00x0S-1-0-0NASEnaseNASE0x70f5590egrissel.rosagrissel.rosaS-1-5-21-3822721094-983390456-1902330015-214901NaNnase.ds.army.mil_rucka1sep000002_security_2020_09_20_rucka1sep000002-security-2020-09-20-20-31-06-795.xml.zipArmy18.4113132020-09-20T15:57:54.001-04:00[America/Puerto_Rico]2020-09-20T15:57:54.001-04:00[America/Puerto_Rico]RUCK-66.124234nase.ds.army.milFORT BUCHANANrucka1sep000002SecurityRUCKA1SEP000002.nase.ds.army.milmil.army.ds.nase.RUCKA1SEP000002462413056754941864107920x80200000000000000054849625-5478-4994-A5BA-3E3B0328C30DMicrosoft-Windows-Security-Auditing125442020-09-20T19:57:54.001Z1
200253eef8973894112a57e1604ace6ce1600626059352evtx-security-cU&FOUOPUSINDOPACOMPRSEA12.0Kerberos%%1833192.86.233.84571390NaNE8105A26-843E-4368-9F22-7201B6396496Kerberos30x00x0S-1-0-0NASEnaseNASE0x660be7feBUCHW1H1AAWK307$buchw1h1aawk307$S-1-5-21-3822721094-983390456-1902330015-14514140NaNnase.ds.army.mil_bucha1sep000002_security_2020_09_20_bucha1sep000002-security-2020-09-20-21-55-11-192.xml.zip,nase.ds.army.mil_bucha1sep000002_security_2020_09_20_bucha1sep000002-security-2020-09-20-23-37-42-647.xml.zipArmy18.4113132020-09-20T14:20:59.352-04:00[America/Puerto_Rico]2020-09-20T14:20:59.352-04:00[America/Puerto_Rico]BUCH-66.124234nase.ds.army.milFORT BUCHANANbucha1sep000002SecurityBUCHA1SEP000002.nase.ds.army.milmil.army.ds.nase.BUCHA1SEP0000024624715859566920134200x80200000000000000054849625-5478-4994-A5BA-3E3B0328C30DMicrosoft-Windows-Security-Auditing125442020-09-20T18:20:59.352Z1
300480d40360a600e7b49b47deb3043dc1600581085422evtx-security-cU&FOUOPUSINDOPACOMPRSEA12.0Kerberos%%1833192.86.233.152540790NaN53A15AD8-B5D7-5B68-0FC7-7CC18C350A34Kerberos30x00x0S-1-0-0NASEnaseNASE0x5e2b5883BUCHW1H1AAWK320$buchw1h1aawk320$S-1-5-21-3822721094-983390456-1902330015-17368875NaNnase.ds.army.mil_bucha1sep000002_security_2020_09_20_bucha1sep000002-security-2020-09-20-09-55-11-873.xml.zip,nase.ds.army.mil_bucha1sep000002_security_2020_09_20_bucha1sep000002-security-2020-09-20-15-09-01-346.xml.zipArmy18.4113132020-09-20T01:51:25.422-04:00[America/Puerto_Rico]2020-09-20T01:51:25.422-04:00[America/Puerto_Rico]BUCH-66.124234nase.ds.army.milFORT BUCHANANbucha1sep000002SecurityBUCHA1SEP000002.nase.ds.army.milmil.army.ds.nase.BUCHA1SEP000002462471544823492066480x80200000000000000054849625-5478-4994-A5BA-3E3B0328C30DMicrosoft-Windows-Security-Auditing125442020-09-20T05:51:25.422Z1
4007d36793dabea537d18a65558262d861600597758040evtx-security-cU&FOUOPUSINDOPACOMPRSEA12.0Kerberos%%1833192.86.233.140578410NaN2C8AD1A6-24AA-82C3-EBE8-276C0AD715C2Kerberos30x00x0S-1-0-0NASEnaseNASE0x6104fb0aulises.marrerodiazulises.marrerodiazS-1-5-21-3822721094-983390456-1902330015-13405329NaNnase.ds.army.mil_bucha1sep000002_security_2020_09_20_bucha1sep000002-security-2020-09-20-15-09-01-346.xml.zipArmy18.4113132020-09-20T06:29:18.040-04:00[America/Puerto_Rico]2020-09-20T06:29:18.040-04:00[America/Puerto_Rico]BUCH-66.124234nase.ds.army.milFORT BUCHANANbucha1sep000002SecurityBUCHA1SEP000002.nase.ds.army.milmil.army.ds.nase.BUCHA1SEP0000024624715548538920132080x80200000000000000054849625-5478-4994-A5BA-3E3B0328C30DMicrosoft-Windows-Security-Auditing125442020-09-20T10:29:18.04Z1
500c09a4a1df614cfa63e46364eff82fe1600595581480evtx-security-cU&FOUOPUSINDOPACOMPRSEA11.0Kerberos%%1833192.86.236.70502670NaN780ABBB6-3DE9-DDC4-0E75-36042B1A1CA4Kerberos30x00x0S-1-0-0NASEnaseNASE0xbdbef16cinkyung.a.walkerinkyung.a.walkerS-1-5-21-3822721094-983390456-1902330015-17026598NaNnase.ds.army.mil_bucha1sep000001_security_2020_09_20_bucha1sep000001-security-2020-09-20-09-55-10-648.xml.zip,nase.ds.army.mil_bucha1sep000001_security_2020_09_20_bucha1sep000001-security-2020-09-20-17-14-37-028.xml.zipArmy18.4113132020-09-20T05:53:01.480-04:00[America/Puerto_Rico]2020-09-20T05:53:01.480-04:00[America/Puerto_Rico]BUCH-66.124234nase.ds.army.milFORT BUCHANANbucha1sep000001SecurityBUCHA1SEP000001.nase.ds.army.milmil.army.ds.nase.BUCHA1SEP000001462475594670793291880x80200000000000000054849625-5478-4994-A5BA-3E3B0328C30DMicrosoft-Windows-Security-Auditing125442020-09-20T09:53:01.48Z1
600e0675c96dab6c8704718aba606e4d81600603097152evtx-security-cU&FOUOPUSINDOPACOMPRSEA11.0Kerberos%%1833192.86.233.14632070NaN3B3CF9A4-B4B0-7421-4B57-2821078B9FF3Kerberos30x00x0S-1-0-0NASEnaseNASE0xbf4cc8daBUCHW6XDAAWK016$buchw6xdaawk016$S-1-5-21-3822721094-983390456-1902330015-17681883NaNnase.ds.army.mil_bucha1sep000001_security_2020_09_20_bucha1sep000001-security-2020-09-20-17-14-37-028.xml.zipArmy18.4113132020-09-20T07:58:17.152-04:00[America/Puerto_Rico]2020-09-20T07:58:17.152-04:00[America/Puerto_Rico]BUCH-66.124234nase.ds.army.milFORT BUCHANANbucha1sep000001SecurityBUCHA1SEP000001.nase.ds.army.milmil.army.ds.nase.BUCHA1SEP0000014624755988939932127560x80200000000000000054849625-5478-4994-A5BA-3E3B0328C30DMicrosoft-Windows-Security-Auditing125442020-09-20T11:58:17.152Z1
700e131900ab1b8d8952825175f1b73191600604132645evtx-security-cU&FOUOPUSINDOPACOMPRSEA11.0Kerberos%%1833192.86.233.214618290NaN14B2E4DA-66EE-9E06-A322-059EC5BB25D9Kerberos30x00x0S-1-0-0NASEnaseNASE0xbf7c5928BUCHW1H1AAWK215$buchw1h1aawk215$S-1-5-21-3822721094-983390456-1902330015-15196847NaNnase.ds.army.mil_bucha1sep000001_security_2020_09_20_bucha1sep000001-security-2020-09-20-17-14-37-028.xml.zipArmy18.4113132020-09-20T08:15:32.645-04:00[America/Puerto_Rico]2020-09-20T08:15:32.645-04:00[America/Puerto_Rico]BUCH-66.124234nase.ds.army.milFORT BUCHANANbucha1sep000001SecurityBUCHA1SEP000001.nase.ds.army.milmil.army.ds.nase.BUCHA1SEP0000014624755995959932136280x80200000000000000054849625-5478-4994-A5BA-3E3B0328C30DMicrosoft-Windows-Security-Auditing125442020-09-20T12:15:32.645Z1
800fdeca56990aa72fc28dfea5f3189541600627362648evtx-security-cU&FOUOPUSINDOPACOMPRSEA12.0Kerberos%%1833192.86.234.72578620NaNB0935571-FADA-6169-8FE4-2E33919C7B7EKerberos30x00x0S-1-0-0NASEnaseNASE0x66514ecbBUCHW6SAAAWK013$buchw6saaawk013$S-1-5-21-3822721094-983390456-1902330015-14891726NaNnase.ds.army.mil_bucha1sep000002_security_2020_09_20_bucha1sep000002-security-2020-09-20-21-55-11-192.xml.zip,nase.ds.army.mil_bucha1sep000002_security_2020_09_20_bucha1sep000002-security-2020-09-20-23-37-42-647.xml.zipArmy18.4113132020-09-20T14:42:42.648-04:00[America/Puerto_Rico]2020-09-20T14:42:42.648-04:00[America/Puerto_Rico]BUCH-66.124234nase.ds.army.milFORT BUCHANANbucha1sep000002SecurityBUCHA1SEP000002.nase.ds.army.milmil.army.ds.nase.BUCHA1SEP0000024624715876836920131160x80200000000000000054849625-5478-4994-A5BA-3E3B0328C30DMicrosoft-Windows-Security-Auditing125442020-09-20T18:42:42.648Z1
9010759e7708529bc6f881eeab31160de1600601042920evtx-security-cU&FOUOPUSINDOPACOMPRSEA11.0Kerberos%%1833192.86.236.70511840NaNCECFF3B6-4780-DEFE-7162-89C08FB81DB2Kerberos30x00x0S-1-0-0NASEnaseNASE0xbed2060finkyung.a.walkerinkyung.a.walkerS-1-5-21-3822721094-983390456-1902330015-17026598NaNnase.ds.army.mil_bucha1sep000001_security_2020_09_20_bucha1sep000001-security-2020-09-20-17-14-37-028.xml.zipArmy18.4113132020-09-20T07:24:02.920-04:00[America/Puerto_Rico]2020-09-20T07:24:02.920-04:00[America/Puerto_Rico]BUCH-66.124234nase.ds.army.milFORT BUCHANANbucha1sep000001SecurityBUCHA1SEP000001.nase.ds.army.milmil.army.ds.nase.BUCHA1SEP0000014624755977069932136280x80200000000000000054849625-5478-4994-A5BA-3E3B0328C30DMicrosoft-Windows-Security-Auditing125442020-09-20T11:24:02.92Z1

Last rows

IdTimestampData TypeVisibilityARCHITECTURECOCOM_EVENTDATA_DATA_IPADDRESSCOUNTRY_EVENTDATA_DATA_IPADDRESSDOMAINDOMAINCONTROLLERDOMAINCONTROLLERNUMBEREVENTDATA_DATA_AUTHENTICATIONPACKAGENAMEEVENTDATA_DATA_IMPERSONATIONLEVELEVENTDATA_DATA_IPADDRESSEVENTDATA_DATA_IPPORTEVENTDATA_DATA_KEYLENGTHEVENTDATA_DATA_LMPACKAGENAMEEVENTDATA_DATA_LOGONGUIDEVENTDATA_DATA_LOGONPROCESSNAMEEVENTDATA_DATA_LOGONTYPEEVENTDATA_DATA_PROCESSIDEVENTDATA_DATA_SUBJECTLOGONIDEVENTDATA_DATA_SUBJECTUSERSIDEVENTDATA_DATA_TARGETDOMAINNAMEEVENTDATA_DATA_TARGETDOMAINNAME_LCASEEVENTDATA_DATA_TARGETDOMAINNAME_REVERSEEVENTDATA_DATA_TARGETLOGONIDEVENTDATA_DATA_TARGETUSERNAMEEVENTDATA_DATA_TARGETUSERNAME_LCASEEVENTDATA_DATA_TARGETUSERSIDEVENTDATA_DATA_WORKSTATIONNAMEFILENAME_INGESTIPBRANCHCATEGORY_EVENTDATA_DATA_IPADDRESSLATITUDE_EVENTDATA_DATA_IPADDRESSLOCAL_SYSTEM_TIMECREATED_SYSTEMTIMELOCAL_TIMESTAMPLOCATIONLONGITUDE_EVENTDATA_DATA_IPADDRESSNETWORK_COLLECTIONORGANIZATION_OWNER_EVENTDATA_DATA_IPADDRESSSITE_COLLECTIONSYSTEM_CHANNELSYSTEM_COMPUTERSYSTEM_COMPUTER_REVERSESYSTEM_EVENTIDSYSTEM_EVENTRECORDIDSYSTEM_EXECUTION_PROCESSIDSYSTEM_EXECUTION_THREADIDSYSTEM_KEYWORDSSYSTEM_LEVELSYSTEM_OPCODESYSTEM_PROVIDER_GUIDSYSTEM_PROVIDER_NAMESYSTEM_TASKSYSTEM_TIMECREATED_SYSTEMTIMESYSTEM_VERSION
3296021ff0ac894d3cca3c1712f579408ddb5de1601063046181evtx-security-cU&FOUOPUSINDOPACOMPRSEA11.0Kerberos%%1833192.86.233.95567210NaN050EDB16-7DDF-5B8B-B098-D0DE4408CD8AKerberos30x00x0S-1-0-0NASEnaseNASE0x12540dca9LAND1470627758121004land1470627758121004S-1-5-21-3822721094-983390456-1902330015-18783928NaNnase.ds.army.mil_bucha1sep000001_security_2020_09_25_bucha1sep000001-security-2020-09-25-20-32-57-736.xml.zipArmy18.4113132020-09-25T15:44:06.181-04:00[America/Puerto_Rico]2020-09-25T15:44:06.181-04:00[America/Puerto_Rico]BUCH-66.124234nase.ds.army.milFORT BUCHANANbucha1sep000001SecurityBUCHA1SEP000001.nase.ds.army.milmil.army.ds.nase.BUCHA1SEP000001462476005461393284840x80200000000000000054849625-5478-4994-A5BA-3E3B0328C30DMicrosoft-Windows-Security-Auditing125442020-09-25T19:44:06.181Z1
3296022ff4f1a8f7851bb38c10a2124479f1e771601064353932evtx-security-cU&FOUONaNUSINDOPACOMPRNaNNaNNaNKerberos%%1833192.86.237.51535570NaN7EA05452-3251-980A-F68E-51040D1D9397Kerberos30x00x0S-1-0-0NASEnaseNASE0x4fd63c34BUCHW1H1AANB191$buchw1h1aanb191$S-1-5-21-3822721094-983390456-1902330015-16296211NaNdahq.ds.army.mil_carsa1hqvxd0002_security_2020_09_25_carsa1hqvxd0002-security-2020-09-25-23-55-15-627.xml.zip,dahq.ds.army.mil_carsa1hqvxd0002_security_2020_09_26_carsa1hqvxd0002-security-2020-09-26-00-43-30-208.xml.zipArmy18.4113132020-09-25T16:05:53.932-04:00[America/Puerto_Rico]2020-09-25T16:05:53.932-04:00[America/Puerto_Rico]NaN-66.124234dahq.ds.army.milFORT BUCHANANcarsa1hqvxd0002SecurityCARSA1HQVXD0002.dahq.ds.army.milmil.army.ds.dahq.CARSA1HQVXD0002462470965330475629280x80200000000000000054849625-5478-4994-A5BA-3E3B0328C30DMicrosoft-Windows-Security-Auditing125442020-09-25T20:05:53.932Z1
3296023ff4face363d571e54b0143f3c2faba621601034075309evtx-security-cU&FOUOPUSINDOPACOMPRSEA12.0Kerberos%%1833206.37.251.78511940NaN66CA73C5-CDC3-B439-A800-836C239E140FKerberos30x00x0S-1-0-0NASEnaseNASE0xb39c566eBUCHA7FSGP01$bucha7fsgp01$S-1-5-21-3822721094-983390456-1902330015-10172758NaNnase.ds.army.mil_bucha1sep000002_security_2020_09_25_bucha1sep000002-security-2020-09-25-14-52-06-339.xml.zipArmy18.4113132020-09-25T07:41:15.309-04:00[America/Puerto_Rico]2020-09-25T07:41:15.309-04:00[America/Puerto_Rico]BUCH-66.124234nase.ds.army.milFORT BUCHANANbucha1sep000002SecurityBUCHA1SEP000002.nase.ds.army.milmil.army.ds.nase.BUCHA1SEP000002462471942929692043640x80200000000000000054849625-5478-4994-A5BA-3E3B0328C30DMicrosoft-Windows-Security-Auditing125442020-09-25T11:41:15.309Z1
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3296026ffb8d4e8c6194296ec5ed4072d9da9521600995589057evtx-security-cU&FOUOPUSINDOPACOMPRSEA12.0Kerberos%%1833192.86.233.101643130NaN03715B36-343F-3CAA-B991-03F416F98C5DKerberos30x00x0S-1-0-0NASEnaseNASE0xacac7a62justin.s.mcewenjustin.s.mcewenS-1-5-21-3822721094-983390456-1902330015-11129376NaNnase.ds.army.mil_bucha1sep000002_security_2020_09_25_bucha1sep000002-security-2020-09-25-01-24-32-488.xml.zipArmy18.4113132020-09-24T20:59:49.057-04:00[America/Puerto_Rico]2020-09-24T20:59:49.057-04:00[America/Puerto_Rico]BUCH-66.124234nase.ds.army.milFORT BUCHANANbucha1sep000002SecurityBUCHA1SEP000002.nase.ds.army.milmil.army.ds.nase.BUCHA1SEP0000024624719178052920196360x80200000000000000054849625-5478-4994-A5BA-3E3B0328C30DMicrosoft-Windows-Security-Auditing125442020-09-25T00:59:49.057Z1
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3296030ffd1317c919e2cf788e629aac073ca0f1601003533594evtx-security-cU&FOUOPUSINDOPACOMPRSEA12.0Kerberos%%1833192.86.236.27566990NaNC7744321-773E-BF02-20FB-F3FF41BEDD43Kerberos30x00x0S-1-0-0NASEnaseNASE0xae119eb2BUCHW6XDAAWK011$buchw6xdaawk011$S-1-5-21-3822721094-983390456-1902330015-14893669NaNnase.ds.army.mil_bucha1sep000002_security_2020_09_25_bucha1sep000002-security-2020-09-25-09-55-13-152.xml.zip,nase.ds.army.mil_bucha1sep000002_security_2020_09_25_bucha1sep000002-security-2020-09-25-14-52-06-339.xml.zipArmy18.4113132020-09-24T23:12:13.594-04:00[America/Puerto_Rico]2020-09-24T23:12:13.594-04:00[America/Puerto_Rico]BUCH-66.124234nase.ds.army.milFORT BUCHANANbucha1sep000002SecurityBUCHA1SEP000002.nase.ds.army.milmil.army.ds.nase.BUCHA1SEP000002462471922665892016440x80200000000000000054849625-5478-4994-A5BA-3E3B0328C30DMicrosoft-Windows-Security-Auditing125442020-09-25T03:12:13.594Z1